Ethical risks of AI-based adaptive e-learning systems and a conceptual model for their minimization in higher education

Authors

DOI:

https://doi.org/10.15587/2519-4984.2026.363030

Keywords:

ethical risks, adaptive learning, artificial intelligence, digital ethics, academic integrity, e-learning

Abstract

This article looks at ethical risks that arise when AI-based adaptive e-learning systems are used in higher education. These tools are usually discussed in terms of better individualization of learning. That is true to an extent, their use is still not neutral. Alongside clear benefits, less visible issues start to appear. Some are familiar, for example protection of personal data, algorithmic bias. Others are harder to pin down: a gradual shift in teacher autonomy, changes in how teachers and students interact, and a growing dependence on automated decisions. Taken together, these changes affect not only the technical side of education, but also how learning is organized. The discussion draws on international frameworks, including the UNESCO Recommendation on the Ethics of Artificial Intelligence and the General Data Protection Regulation (GDPR), as well as recent work in digital pedagogy. In this context, the notion of an “ethical risk of an adaptive e-learning system” is revisited and organized into four groups: risks related to protection of personal data and security, algorithmic bias, reduced autonomy of participants, and educational inequality.

The study combines several approaches: theoretical analysis, comparison of international practices, and conceptual modeling. The resulting model is not meant to be universal. Rather, it is a framework that can be adjusted to a specific institutional context, it includes four stages: an ethical audit, pedagogical adjustment of algorithms and content, development of digital-ethical competence, and current monitoring with feedback. One difficulty, as it seems, is not the lack of tools but the way they are used. Technical, pedagogical, and legal approaches often remain separate. Because of this, outcomes depend heavily on context, including how national strategies for changes driven by digital technologies in education are interpreted in practice.

The results may be useful for universities developing internal AI policies, designing courses in digital ethics, and building adaptive e-learning environments

Author Biography

Nazar Hryshchuk, Ternopil Volodymyr Hnatiuk National Pedagogical University

PhD Student

Department of Informatics and Methods of its Teaching

References

  1. Holmes, W., Bialik, M., Fadel, C. (2022). Artificial Intelligence in Education. Boston: Center for Curriculum Redesign, 168.
  2. Recommendation on the Ethics of Artificial Intelligence (2021). Paris: UNESCO Publishing, 21. Available at: https://unesdoc.unesco.org/ark:/48223/pf0000380455
  3. Jobin, A., Ienca, M., Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1 (9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
  4. Smuha, N. A. (2020). Beyond a Human Rights-Based Approach to AI Governance: Promise, Pitfalls, Plea. Philosophy & Technology, 34 (S1), 91–104. https://doi.org/10.1007/s13347-020-00403-w
  5. Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Cham: Springer, 244. https://doi.org/10.1007/978-3-030-30371-6
  6. GDPR. General Data Protection Regulation (EU) 2016/679. Official Journal of the European Union. 2016. L 119/1. Available at: https://gdpr-info.eu/
  7. MON Ukrainy. Kontseptsiia rozvytku shtuchnoho intelektu v Ukraini (2020). Nakaz MON Ukrainy No. 1556. 02.12.2020. Available at: https://itschool.oano.od.ua/uk/site/kontseptsiya-rozvitku-sht.html
  8. Zawacki-Richter, O., Marín, V. I., Bond, M., Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16 (1). https://doi.org/10.1186/s41239-019-0171-0
  9. Roll, I., Wylie, R. (2016). Evolution and Revolution in Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 26 (2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
  10. Floridi, L. (2023). The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities. Oxford: Oxford University Press, 416. https://doi.org/10.1093/oso/9780198883098.001.0001
  11. Volynets, V., Trach, Y. (2025). Ethical Awareness of Youth about Artificial Intelligence: Education, Risks, Regulation. Digital Platform: Information Technologies in Sociocultural Sphere, 8 (2), 289–298. https://doi.org/10.31866/2617-796x.8.2.2025.347871
  12. Shneiderman, B. (2022). Human-Centered AI. Oxford: Oxford University Press, 352. https://doi.org/10.1093/oso/9780192845290.001.0001
  13. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B. et al. (2020). Closing the AI accountability gap. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 33–44. https://doi.org/10.1145/3351095.3372873
  14. Baker, R. S., Hawn, A. (2022). Algorithmic Bias in Education. International Journal of Artificial Intelligence in Education, 32 (4), 1052–1092. https://doi.org/10.1007/s40593-021-00285-9
  15. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3 (2). https://doi.org/10.1177/2053951716679679
  16. Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H. et al. (2020). Learning analytics in European higher education – Trends and barriers. Computers & Education, 155, 103933. https://doi.org/10.1016/j.compedu.2020.103933
  17. Ferguson, C., van den Broek, E. L., van Oostendorp, H. (2022). AI-Induced guidance: Preserving the optimal Zone of Proximal Development. Computers and Education: Artificial Intelligence, 3, 100089. https://doi.org/10.1016/j.caeai.2022.100089
  18. Understanding the needs of Ukrainian teacher training institutions: UNESCO policy paper (2025). UNESCO, 23. Available at: https://unesdoc.unesco.org/ark:/48223/pf0000393188

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Published

2026-05-29

How to Cite

Hryshchuk, N. (2026). Ethical risks of AI-based adaptive e-learning systems and a conceptual model for their minimization in higher education. ScienceRise: Pedagogical Education, (2(67), 61–67. https://doi.org/10.15587/2519-4984.2026.363030

Issue

No. 2(67) (2026)

Section

Pedagogical Education

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Copyright (c) 2026 Nazar Hryshchuk

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